Animal husbandry and veterinary intelligent feed management system based on automatic control

Abstract

The invention discloses a livestock veterinary intelligent feed management system based on automatic control, and particularly relates to the field of automatic control. Comprising a real-time data acquisition module, a data preprocessing module, a feed feeding reference analysis module, a feed putting and monitoring module, a feed intake analysis module, a feed proportion adjustment module and a management report output module. According to the animal husbandry veterinary intelligent feed management system based on automatic control, various information acquisition devices are deployed in a livestock farm feed management area through a real-time data acquisition module, and data are acquired according to set parameters, so that the real-time monitoring capability and the data acquisition fineness are improved; an intelligent feed intake analysis model is constructed through a feed feeding reference analysis module, a normal feed intake reference is established, and the reasonable feeding amount is accurately determined; and the feed intake analysis module is used for calculating the feed intake deviation proportion and trend, judging the feed intake change and providing a proportion adjustment basis.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and more specifically, to an intelligent feed management system for animal husbandry and veterinary medicine based on automation control. Background Art

[0002] With the rapid development of modern agricultural technology, in the scenario of large-scale animal husbandry, it is particularly important to achieve efficient management and scientific allocation of feed. At the same time, as one of the important factors affecting livestock health, the management of feed feeding needs to be more scientific and precise; automated control technology and information technology have promoted the innovation of animal husbandry and veterinary management systems, making precise feeding and health management a reality.

[0003] Traditional manual feed management methods are not only inefficient but also difficult to ensure accuracy and timeliness, and cannot meet the growing breeding needs. The intelligent feed management system can accurately control the amount of feed put in, reduce manual intervention, reduce the possibility of human error, and improve work efficiency, allowing farm managers to devote more energy to other key business activities.

[0004] However, in actual use, it still has some shortcomings, such as the lack of real-time monitoring and dynamic adjustment capabilities, and insufficient flexibility in responding to emergencies or special needs; poor compatibility, difficulty in achieving seamless data sharing and collaborative work, resulting in inconsistent and inefficient management; and the lack of precise control over feed quality, making it impossible to monitor and warn of abnormal feed conditions in real time. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent feed management system for animal husbandry and veterinary medicine based on automatic control, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solutions: An intelligent feed management system for animal husbandry and veterinary medicine based on automatic control, characterized in that it includes a system operation database, a system central processing module and a user information terminal, and also includes: Real-time data acquisition module: used to respond to the information acquisition equipment installed in the livestock farm, obtain the first feed data, and transmit it to the data preprocessing module; Data preprocessing module: used for preprocessing the first feed data transmitted by the real-time data acquisition module, the preprocessing operation is used for obtaining the first feed impact group corresponding to the first feed data, and transmitting it to the feed feeding benchmark analysis module; Feeding benchmark analysis module: obtains an intelligent feed intake analysis model based on the first feed impact group transmitted by the data preprocessing module, establishes a normal feed intake benchmark according to the intelligent feed intake analysis model to obtain second feed data, and transmits it to the feed delivery and monitoring module and the feed intake analysis module; Feed delivery and monitoring module: used to control the feed delivery amount according to the second feed data transmitted by the feed delivery benchmark analysis module, and record the third feed data through the monitoring device and transmit it to the feed intake analysis module; Feed intake analysis module: used to integrate the second feed data transmitted by the feed feeding benchmark analysis module and the third feed data transmitted by the feed delivery and monitoring module, analyze the change of feed intake to obtain the second feed impact group, and transmit it to the feed ratio adjustment module; Feed ratio adjustment module: used to obtain an intelligent feed ratio adjustment model, dynamically adjust the feed ratio through the second feed impact group transmitted by the feed intake analysis module, and transmit it to the management report output module; Management report output module: used to integrate the data information of each module in the intelligent feed management system and display it in the form of management report; The system operation database includes all data texts of the intelligent feed management system and collects the information text output by each module in real time. The system central processing module is used for the information text instructions output by each module in the central control system. The user information terminal is an information output device for receiving the intelligent feed management system.

[0007] Preferably, the real-time data acquisition module acquires the first feed data, specifically including: Set the basic collection parameters of each information collection device through the user interface of the intelligent feed management system to start the self-test program; Sending a collection instruction to each information collection device at a preset time interval, collecting the corresponding first feed data in the target livestock farm in real time, and storing it in the system buffer area; The system buffer is a temporary data storage area different from the system operation database, which is transmitted to the data preprocessing module.

[0008] Preferably, the feed feeding benchmark analysis module includes an analysis model parameter configuration unit, an analysis model training unit, a normal feed intake benchmark establishment unit, and a data transmission unit; The analysis model parameter configuration unit is used to load the first feed impact group transmitted by the data preprocessing module, and determine the preset training environment of the intelligent feed intake analysis model, the preset training environment is the corresponding parameter setting operation during the operation of the intelligent feed intake analysis model, and the parameter setting operation includes model training algorithm selection, model evaluation index, data division ratio, and model update frequency; The analysis model training unit inputs the intelligent feed intake analysis model for training based on the corresponding historical feeding data stored in the system operation database; The normal feed intake benchmark establishing unit is used to generate normal feed intake benchmark data under different conditions, namely, second feed data, through an intelligent feed intake analysis model.

[0009] The data transmission unit is used to store the generated second feed data in a system cache area using a hash table structure, and transmit the second feed data when a request data signal from the feed delivery and monitoring module and the feed intake analysis module is detected.

[0010] Preferably, the feed feeding benchmark analysis module obtains the second feed data, specifically including: Performing a feed intake labeling operation on the first feed impact group, wherein the feed intake labeling operation is used to determine the actual feed intake value under specific environmental conditions and livestock physiological states, and setting it as a target value for model training; Based on the growth status and environmental conditions corresponding to the target detected livestock species and the first feed impact group, a feed intake benchmark input matrix is ​​constructed, and the feed intake benchmark input matrix is ​​an input form of the target detected livestock species input into the intelligent feed intake analysis model; Input the feed intake benchmark input matrix into the intelligent feed intake analysis model to obtain the normal feed intake benchmark; Set a normal feed intake baseline fluctuation range.

[0011] Preferably, in the feed delivery and monitoring module, the third feed data includes actual feed intake, feeding time, feed ratio, and feed delivery amount.

[0012] Preferably, the feed intake analysis module obtains the second feed impact group, specifically including: Based on the current time tn, set the time decay function w(t j ), specifically expressed as: , Among them, e is a constant with a value of 2.7183, λ is the time decay coefficient, j is the index of the time series, and t j It is expressed as the jth moment time; Based on the time decay function w(t j ), the actual feed intake FIa corresponding to the third feed data, and the normal feed intake benchmark Fib corresponding to the second feed data, calculate the feed intake deviation ratio Pfi, which is specifically expressed as: , Among them, FIa represents the actual feed intake corresponding to the third feed data, FIb represents the normal feed intake benchmark corresponding to the second feed data, m represents the total number of time series, j represents the index of the time series, t j Expressed as the jth moment, w(t j） ) is expressed as a time decay function.

[0013] Preferably, the feed intake analysis module obtains the second feed impact group, specifically including: Based on the trend analysis of feed intake data in the time window, the actual feed intake data sequence in the time window is {FIa1, FIa2, ..., FIa k}, then within the time window T, the calculation formula for the feed intake trend Tfi is specifically expressed as: Tfi = (FIa tn-T +FIa tn-T+1 +…+FIa T ) / T, Among them, tn represents the current time, and T represents the length of the time window.

[0014] Preferably, the feed ratio adjustment module dynamically adjusts the feed ratio, specifically including: Input the data of the second feed impact group into the intelligent feed ratio adjustment model to evaluate the adjustment requirements of the current feed formula, where the adjustment requirements are the impact quantification results obtained by the intelligent feed ratio adjustment model based on the livestock feeding behavior characteristics, environmental parameters, and physiological state corresponding to the second feed impact group; Adjust the current feed formula based on the current feed ratio and the needs of animal husbandry veterinarians; Establish a closed-loop feedback mechanism to continuously monitor actual feeding conditions and livestock health after adjusting feed ratios.

[0015] Technical effects and advantages of the present invention: The present invention deploys a variety of information collection devices in the livestock farm feed management area through a real-time data collection module and collects data according to set parameters, thereby improving the real-time monitoring capability and data collection precision; The present invention constructs an intelligent feed intake analysis model through a feed feeding benchmark analysis module, establishes a normal feed intake benchmark, and accurately determines a reasonable feeding amount; The present invention calculates the feed intake deviation ratio and trend through a feed intake analysis module, determines the feed intake change and provides a basis for ratio adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a system flow chart of the present invention; Figure 2 It is a system step diagram of the present invention; Figure 3 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0019] In the following, the terms "first", "second", and "third" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", and "third" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0020] As attached Figure 1 The animal husbandry and veterinary intelligent feed management system based on automatic control shown in the figure includes a system operation database, a system central processing module and a user information terminal, and also includes a real-time data acquisition module, a data preprocessing module, a feed feeding benchmark analysis module, a feed delivery and monitoring module, a feed intake analysis module, a feed ratio adjustment module, and a management report output module.

[0021] The system operation database includes all data texts of the intelligent feed management system and collects the information text output by each module in real time. The system central processing module is used for the information text instructions output by each module in the central control system. The user information terminal is an information output device for receiving the livestock and veterinary intelligent feed management system.

[0022] The real-time data acquisition module is used to respond to the information acquisition equipment installed in the livestock farm, obtain the first feed data, and transmit it to the data preprocessing module.

[0023] Specifically, in the feed management area of ​​the target livestock farm, a variety of information collection equipment is deployed according to the specific needs and layout of the target livestock farm. The information collection equipment includes but is not limited to temperature sensors, humidity sensors, carbon dioxide sensors, etc., which monitor the environmental parameters in the livestock farm and the physiological parameters of the livestock in real time. The physiological parameters include but are not limited to body temperature, heart rate, weight, etc.; they are integrated to generate the first feed data, which includes temperature, humidity, carbon dioxide concentration, and the corresponding body temperature, heart rate, and weight of the livestock.

[0024] In this embodiment, a temperature sensor with an accuracy of ±0.5°C and a humidity sensor with an accuracy of ±3%RH are installed in the feed management area of ​​the target livestock farm, that is, inside the feed warehouse, and data is fed back to the real-time data acquisition module once an hour via a wireless Wi-Fi connection.

[0025] In a possible implementation, obtaining the first feed data includes: setting basic collection parameters of each information collection device through the user interface of the intelligent feed management system, the basic collection parameters include but are not limited to sampling frequency, range, alarm threshold, data storage format, etc., starting a self-test program, the self-test program is an initial test set by the intelligent feed management system for the information collection device, the initial test is to check the connection status of each information collection device and load the preset basic collection parameters; the system central processing module sends a collection instruction to each information collection device at a time interval corresponding to the preset basic collection parameters, so as to collect the corresponding first feed data in the target livestock farm in real time, and store it in the system cache area, the system cache area is a temporary data storage area different from the system operation database, and is transmitted to the data preprocessing module.

[0026] In this embodiment, the system cache area adopts a first-in-first-out circular cache structure, and the cache capacity is set to store 1000 data items; at the same time, when the amount of data in the system cache area reaches 50 items, a data transmission operation is immediately started.

[0027] The data preprocessing module is used to perform preprocessing operations on the first feed data transmitted by the real-time data acquisition module. The preprocessing operation is used to obtain the first feed impact group corresponding to the first feed data and transmit it to the feed feeding benchmark analysis module.

[0028] Specifically, after obtaining the first feed data transmitted by the real-time data acquisition module, the intelligent feed management system initializes and cleans the system buffer area and performs preprocessing operations on the first feed data to obtain multiple key impact information corresponding to the first feed data, namely, the first feed impact group. The preprocessing steps are as follows: Data verification check: The format and range of the received first feed data are verified, and the data records that fail to pass are marked as invalid and directly discarded. The range of the numerical value is directly checked for environmental parameter data; the livestock physiological parameter data is verified according to the normal physiological range of different livestock species. Data cleaning: Based on the alarm threshold set by the user interface, the slightly abnormal data that exceeds the normal range but is within the error range is corrected using a data smoothing algorithm; for data missing, data completely beyond the reasonable range, and data with obvious errors, the seriously abnormal data is marked as invalid data and deleted from the buffer area, and manual verification is performed at the same time. Feature extraction: The statistical principle is used to calculate the average, maximum, minimum, and standard deviation in each time window. The time window is a data analysis time range set based on the user's analysis needs, including but not limited to daily, weekly, monthly, etc.; the association rules of the first feed data are analyzed according to the machine learning algorithm to determine the influencing factors of feed consumption. Data grouping: Based on the source, type, and feature extraction results of the first feed data, a first feed impact group is constructed, and the first feed impact group includes multiple groups of key variables and weights that affect feed consumption.

[0029] In this embodiment, data related to the environment are divided into environmental impact groups, and data related to livestock physiology are divided into livestock physiology impact groups, and a data group identifier, data source description, and data processing timestamp are added to the data of each impact group.

[0030] The feed feeding benchmark analysis module obtains an intelligent feed intake analysis model based on the first feed impact group transmitted by the data preprocessing module, establishes a normal feed intake benchmark according to the intelligent feed intake analysis model to obtain the second feed data, and transmits it to the feed delivery and monitoring module and the feed intake analysis module.

[0031] Specifically, the intelligent feed intake analysis model is a pre-constructed learning model. By inputting the first feed impact group into the intelligent feed intake analysis model, the intelligent feed intake analysis model obtains the second feed data according to the first feed impact group, and transmits the second feed data to the feed delivery and monitoring module and the feed intake analysis module.

[0032] In a possible implementation, the feed feeding benchmark analysis module includes an analysis model parameter configuration unit, an analysis model training unit, a normal feed intake benchmark establishment unit, and a data transmission unit.

[0033] The analysis model parameter configuration unit is used to load the first feed impact group transmitted by the data preprocessing module and determine the preset training environment of the intelligent feed intake analysis model. The preset training environment is the corresponding parameter setting operation during the operation of the intelligent feed intake analysis model. The parameter setting operation includes model training algorithm selection, model evaluation index, data division ratio, and model update frequency.

[0034] It should be noted that the model training algorithm selection includes but is not limited to multivariate linear regression, decision tree, random forest, support vector machine, neural network, etc.; model evaluation indicators include but are not limited to mean square error, mean absolute error, R² score, etc.

[0035] The analysis model training unit inputs the intelligent feed intake analysis model for training based on the corresponding historical feeding data stored in the system operation database.

[0036] It should be noted that the analysis model training unit performs feed intake labeling operation on the first feed impact group. The feed intake labeling operation is used to determine the actual feed intake value under specific environmental conditions and livestock physiological conditions, and set it as the target value for model training; the data of the first feed impact group is divided into a training set, a validation set, and a test set, the training set data is used to train the intelligent feed intake analysis model, the validation set data is used to evaluate the model, and the loss value and accuracy index on the validation set are calculated.

[0037] In this embodiment, through the feed intake labeling, it can be obtained that when the ambient temperature is between 20 and 25°C, the humidity is between 40% and 60%, and the weight change rate of the detected livestock is greater than 0.5% / day, the average daily feed intake of the livestock is 5 to 6 kg; the intelligent feed intake analysis model adopts an architecture that combines a long short-term memory network with a multi-layer perceptron, and the division ratios of the training set, validation set, and test set are set to 70%, 20%, and 10%, respectively.

[0038] The normal feed intake benchmark establishing unit is used to generate normal feed intake benchmark data under different conditions, i.e., second feed data, through an intelligent feed intake analysis model, including the normal feed intake benchmark and the normal feed intake benchmark fluctuation range of each livestock breed under corresponding growth status and environmental conditions.

[0039] In a possible implementation, based on the growth status and environmental conditions corresponding to the target detected livestock species and the first feed impact group, a feed intake benchmark input matrix is ​​constructed, and the feed intake benchmark input matrix is ​​the input form of the target detected livestock species input into the intelligent feed intake analysis model; the feed intake benchmark input matrix is ​​input into the intelligent feed intake analysis model to obtain a normal feed intake prediction value, that is, a normal feed intake benchmark under different conditions; a normal feed intake benchmark fluctuation range is set, and the fluctuation range is determined by the mean ± standard deviation method, and the standard deviation is calculated based on the error distribution of the intelligent feed intake analysis model on the test set.

[0040] In this embodiment, fattening pigs are used as an example, LW represents the livestock weight, RW represents the weight change rate, ET represents the ambient temperature, and EH represents the ambient humidity. The feed intake reference input matrix is ​​represented by (LW, RW, ET, EH) T Specifically, for fattening pigs weighing 50-100 kg, with a weight change rate of 0.3% / day, an ambient temperature of 22°C, and a humidity of 50%, the intelligent feed intake analysis model corresponding to the architecture combining long short-term memory network and multi-layer perceptron is used to judge that the normal daily feed intake is [4.3, 4.7] kg, where the standard deviation corresponding to the normal feed intake baseline fluctuation range is recorded as Bfe, and the calculation formula is specifically expressed as: , Among them, n represents the number of data in the test set corresponding to the intelligent feed intake analysis model in the analysis model training unit, i represents the index of the data in the test set, and e i Represented as the i-th data error data.

[0041] The data transmission unit is used to store the generated second feed data in a system cache area using a hash table structure, and transmit the second feed data when a request data signal from the feed delivery and monitoring module and the feed intake analysis module is detected.

[0042] The feed delivery and monitoring module is used to control the feed delivery amount according to the second feed data transmitted by the feed delivery benchmark analysis module, and to record the third feed data through the monitoring device and transmit it to the feed intake analysis module.

[0043] Specifically, the second feed data transmitted by the feed feeding benchmark analysis module is received and the feed delivery amount is controlled through the feed delivery equipment; the nutritional components of the feed are detected by evenly installing weight sensors at the bottom of the feed edible bin and setting a near-infrared spectral component detection sensor in the feed preparation area to obtain the third feed data, which includes the actual feed intake, eating time, feed ratio, and feed delivery amount; the generated third feed data is stored in the system cache area using a hash table structure, and the third feed data is transmitted when the request data signal of the feed intake analysis module is detected.

[0044] In this embodiment, a resistive strain sensor with an accuracy of ±0.1% of the full scale is used in the feed storage bin, which is connected to the feed delivery and monitoring module through the ZigBee protocol, and is set to be sampled once every 30 minutes to monitor the feed consumption; a near-infrared spectral component detection sensor with a wavelength range of 800-2500nm is set in the feed preparation area, which communicates with the feed delivery and monitoring module through a USB interface, and detects the corresponding protein, fat, and fiber in the feed at a frequency of 1 to 2 groups.

[0045] The feed intake analysis module is used to analyze the change of feed intake by integrating the second feed data transmitted by the feed feeding benchmark analysis module and the third feed data transmitted by the feed delivery and monitoring module to obtain the second feed impact group and transmit it to the feed ratio adjustment module.

[0046] Specifically, the feed intake analysis module sends a data request signal to the feed feeding benchmark analysis module and the feed delivery and monitoring module, and analyzes the changes in livestock feed intake based on the second feed data and the third feed data to obtain a second feed impact group, which includes a feed intake deviation ratio and a feed intake trend.

[0047] In a possible implementation, obtaining the second feed impact group includes: based on the actual feed intake record corresponding to the third feed data, matching the corresponding livestock identification, collection time, and environmental information, searching for the corresponding normal feed intake benchmark data and fluctuation range in the second feed data cache area; Based on the current time tn, set the time decay function w(t j ), specifically expressed as: , Among them, e is a constant with a value of 2.7183, λ is the time decay coefficient, j is the index of the time series, and t j It is expressed as the jth moment time; It should be noted that the time decay coefficient is the degree of importance that animal husbandry and veterinary medicine attaches to the data in the time series. In this embodiment, the value of the time decay coefficient λ is 0.5; Based on the time decay function w(t j ), the actual feed intake FIa corresponding to the third feed data, and the normal feed intake benchmark Fib corresponding to the second feed data, calculate the feed intake deviation ratio Pfi, which is specifically expressed as: , where FIa represents the actual feed intake corresponding to the third feed data, FIb represents the normal feed intake benchmark corresponding to the second feed data, m represents the total number of time series, j represents the index of the time series, t j represents the time at the j-th moment, and w(t j） ) represents the time decay function; It should be noted that when |Pfi| > 30%, the actual feed intake is marked as abnormal data; Based on the feed intake data within the time window for trend analysis, let the actual feed intake data sequence within the time window be {FIa1, FIa2,..., FIa k}, then within the time window T, the calculation formula for the feed intake trend Tfi is specifically expressed as: Tfi = (FIa tn-T + FIa tn-T+1 +... + FIa T ) / T, where tn represents the current time and T represents the length of the time window; In this embodiment, when the length of the time window T = 5 and tn = 10, the feed intake data sequence within these 5 days from the 5th day to the 10th day is {FIa5 = 2kg, FIa6 = 2.2kg, FIa7 = 1.8kg, FIa8 = 2.1kg, FIa9 = 1.9kg}, then it can be calculated that Tfi = 2kg; let Tfi1 from the 1st day to the 5th day be 1.8kg, and TFi2 from the 5th day to the 10th day be 2kg, that is, the feed intake shows an upward trend; It should be noted that by continuously updating the time window to calculate the feed intake trend, to dynamically track the change trend of the feed intake and obtain whether the feed intake is short-term fluctuating or long-term and continuous change; in this embodiment, by comparing Tfi1 and TFi2 to judge the feed intake trend, when Tfi2 > Tfi1, the feed intake shows an upward trend, when Tfi2 < Tfi1, the feed intake shows a downward trend, and when Tfi2 = Tfi1, the feed intake basically remains stable.

[0048] The feed ratio adjustment module is used to obtain the intelligent feed ratio adjustment model, dynamically adjust the feed ratio through the second feed influence group transmitted by the feed intake analysis module, and transmit it to the management report output module.

[0049] Specifically, the intelligent feed ratio adjustment model is a pre-constructed learning model. By inputting the second feed impact group into the intelligent feed ratio adjustment model, the intelligent feed ratio adjustment model obtains the feed ratio according to the second feed impact group.

[0050] In a possible implementation, dynamically adjusting the feed ratio through the second feed impact group transmitted by the feed intake analysis module includes: inputting the second feed impact group data into the intelligent feed ratio adjustment model to evaluate the adjustment needs of the current feed formula, the adjustment needs are the impact quantification results obtained by the intelligent feed ratio adjustment model based on the livestock feeding behavior characteristics, environmental parameters, and physiological status corresponding to the second feed impact group; and then adjusting the current feed formula based on the current feed ratio and the needs of animal husbandry and veterinary medicine. In this embodiment, taking the random forest regression model as an example, the corresponding decision trees in the intelligent feed ratio adjustment model make decisions based on the input data to obtain the model ratio adjustment ratio; at the same time, a closed-loop feedback mechanism is established to continuously monitor the actual feeding situation and livestock health status after the feed ratio is adjusted.

[0051] The management report output module is used to integrate the data information of each module in the intelligent feed management system and display it in the form of a management report.

[0052] Specifically, the collected livestock physiological data, environmental data, and feed data are stored in corresponding databases respectively; according to the preset report template, key information is extracted from the stored data and integrated, and the report content includes but is not limited to the operating status of each module, the success rate and delay of data collection and transmission, the quantity distribution of different livestock types at each growth stage, the health status based on the change of feed intake and physiological parameters, the deviation analysis of feed feeding at different periods and the normal feeding benchmark, the historical record of feed ratio adjustment, etc.

[0053] As attached Figure 2 The method is a livestock and veterinary intelligent feed management method based on automatic control, comprising: S1: collecting real-time data, S2: preprocessing data, S3: analyzing feed feeding benchmarks, S4: placing and monitoring feed, S5: analyzing feed intake, S6: adjusting feed ratios, and S7: outputting management reports.

[0054] S1: Collecting real-time data: Responding to the information collection equipment installed in the livestock farm, obtaining first feed data; S2: preprocessing data: performing a preprocessing operation on the first feed data, the preprocessing operation is used to obtain a first feed influence group corresponding to the first feed data; S3: Analyze the feed feeding benchmark: obtain an intelligent feed intake analysis model based on the first feed impact group, and establish a normal feed intake benchmark according to the intelligent feed intake analysis model to obtain the second feed data; S4: feeding and monitoring feed: controlling the feed feeding amount according to the second feed data, and recording the third feed data through the monitoring device; S5: Analyze feed intake: Combine the second feed data and the third feed data to analyze the change in feed intake to obtain the second feed impact group; S6: Adjust feed ratio: obtain an intelligent feed ratio adjustment model, and dynamically adjust the feed ratio through the second feed influence group; S7: Output management report: Integrate the data information of each module in the intelligent feed management system and display it in the form of a management report.

[0055] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent feed management system for animal husbandry and veterinary medicine based on automatic control, comprising a system operation database, a system central processing module and a user information terminal, characterized in that: Also includes: Real-time data acquisition module: used to respond to the information acquisition equipment installed in the livestock farm, obtain the first feed data, and transmit it to the data preprocessing module; Data preprocessing module: used for preprocessing the first feed data transmitted by the real-time data acquisition module, the preprocessing operation is used for obtaining the first feed impact group corresponding to the first feed data, and transmitting it to the feed feeding benchmark analysis module; Feeding benchmark analysis module: obtains an intelligent feed intake analysis model based on the first feed impact group transmitted by the data preprocessing module, establishes a normal feed intake benchmark according to the intelligent feed intake analysis model to obtain second feed data, and transmits it to the feed delivery and monitoring module and the feed intake analysis module; Feed delivery and monitoring module: used to control the feed delivery amount according to the second feed data transmitted by the feed delivery benchmark analysis module, and record the third feed data through the monitoring device and transmit it to the feed intake analysis module; Feed intake analysis module: used to integrate the second feed data transmitted by the feed feeding benchmark analysis module and the third feed data transmitted by the feed delivery and monitoring module, analyze the change of feed intake to obtain the second feed impact group, and transmit it to the feed ratio adjustment module; Feed ratio adjustment module: used to obtain an intelligent feed ratio adjustment model, dynamically adjust the feed ratio through the second feed impact group transmitted by the feed intake analysis module, and transmit it to the management report output module; Management report output module: used to integrate the data information of each module in the intelligent feed management system and display it in the form of management report; The system operation database includes all data texts of the intelligent feed management system and collects the information text output by each module in real time. The system central processing module is used for the information text instructions output by each module in the central control system. The user information terminal is an information output device for receiving the intelligent feed management system.

2. The intelligent feed management system for animal husbandry and veterinary medicine based on automatic control according to claim 1 is characterized in that: The real-time data acquisition module obtains the first feed data, specifically including: Set the basic collection parameters of each information collection device through the user interface of the intelligent feed management system to start the self-test program; Sending a collection instruction to each information collection device at a preset time interval, collecting the corresponding first feed data in the target livestock farm in real time, and storing it in the system buffer area; The system buffer is a temporary data storage area different from the system operation database, which is transmitted to the data preprocessing module.

3. The intelligent feed management system for animal husbandry and veterinary medicine based on automatic control according to claim 1 is characterized in that: The feed feeding benchmark analysis module includes an analysis model parameter configuration unit, an analysis model training unit, a normal feed intake benchmark establishment unit, and a data transmission unit; The analysis model parameter configuration unit is used to load the first feed impact group transmitted by the data preprocessing module, and determine the preset training environment of the intelligent feed intake analysis model, the preset training environment is the corresponding parameter setting operation during the operation of the intelligent feed intake analysis model, and the parameter setting operation includes model training algorithm selection, model evaluation index, data division ratio, and model update frequency; The analysis model training unit inputs the intelligent feed intake analysis model for training based on the corresponding historical feeding data stored in the system operation database; The normal feed intake benchmark establishing unit is used to generate normal feed intake benchmark data under different conditions, i.e., second feed data, through an intelligent feed intake analysis model; The data transmission unit is used to store the generated second feed data in a system cache area using a hash table structure, and transmit the second feed data when a request data signal from the feed delivery and monitoring module and the feed intake analysis module is detected.

4. The intelligent feed management system for animal husbandry and veterinary medicine based on automatic control according to claim 3 is characterized by: The feed feeding benchmark analysis module obtains the second feed data, specifically including: Performing a feed intake labeling operation on the first feed impact group, wherein the feed intake labeling operation is used to determine the actual feed intake value under specific environmental conditions and livestock physiological states, and setting it as a target value for model training; Based on the growth status and environmental conditions corresponding to the target detected livestock species and the first feed impact group, a feed intake benchmark input matrix is ​​constructed, and the feed intake benchmark input matrix is ​​an input form of the target detected livestock species input into the intelligent feed intake analysis model; Input the feed intake benchmark input matrix into the intelligent feed intake analysis model to obtain the normal feed intake benchmark; Set a normal feed intake baseline fluctuation range.

5. The intelligent feed management system for animal husbandry and veterinary medicine based on automatic control according to claim 1 is characterized by: In the feed delivery and monitoring module, the third feed data includes actual feed intake, feeding time, feed ratio, and feed delivery amount.

6. The intelligent feed management system for animal husbandry and veterinary medicine based on automatic control according to claim 1, characterized in that: The feed intake analysis module obtains the second feed impact group, specifically including: Based on the current time tn, set the time decay function w(t j ), specifically expressed as: ， Among them, e is a constant with a value of 2.7183, λ is the time decay coefficient, j is the index of the time series, and t j It is expressed as the jth moment time; Based on the time decay function w(t j ), the actual feed intake FIa corresponding to the third feed data, and the normal feed intake benchmark Fib corresponding to the second feed data, calculate the feed intake deviation ratio Pfi, which is specifically expressed as: ， Among them, FIa represents the actual feed intake corresponding to the third feed data, FIb represents the normal feed intake benchmark corresponding to the second feed data, m represents the total number of time series, j represents the index of the time series, t j Expressed as the jth moment, w(t j） ) is expressed as a time decay function.

7. The intelligent feed management system for animal husbandry and veterinary medicine based on automatic control according to claim 1, characterized in that: The feed intake analysis module obtains the second feed impact group, specifically including: Based on the trend analysis of feed intake data in the time window, the actual feed intake data sequence in the time window is {FIa1, FIa2, ..., FIa k }, then within the time window T, the calculation formula for the feed intake trend Tfi is specifically expressed as: Tfi=(FIa tn-T +FIa tn-T+1 +…+FIa T ) / T, Among them, tn represents the current time, and T represents the length of the time window.

8. The intelligent feed management system for animal husbandry and veterinary medicine based on automatic control according to claim 1, characterized in that: The feed ratio adjustment module dynamically adjusts the feed ratio, specifically including: Input the data of the second feed impact group into the intelligent feed ratio adjustment model to evaluate the adjustment requirements of the current feed formula, where the adjustment requirements are the impact quantification results obtained by the intelligent feed ratio adjustment model based on the livestock feeding behavior characteristics, environmental parameters, and physiological state corresponding to the second feed impact group; Adjust the current feed formula based on the current feed ratio and the needs of animal husbandry veterinarians; Establish a closed-loop feedback mechanism to continuously monitor actual feeding conditions and livestock health after adjusting feed ratios.

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